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Intelligent Assistants - A Decision-Theoretic Model: Effective Decision-Theoretic Assistance Through Relational Hierarchical Models - Couverture souple

Natarajan, Sriraam

 
9783639145809: Intelligent Assistants - A Decision-Theoretic Model: Effective Decision-Theoretic Assistance Through Relational Hierarchical Models
Couverture souple
ISBN 10 : 3639145801 ISBN 13 : 9783639145809
Editeur : VDM Verlag, 2009

Synopsis

Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the principles of decision theory to model the general problem of intelligent assistance. We use a combination of hierarchical task models and probabilistic relational languages to specify prior knowledge of the computer assistant. The assistant exploits its prior knowledge to infer the user's goals and takes actions to assist the user. We evaluate the decision theoretic assistance model in three different domains including a real-world domain to demonstrate its generality. We show through experiments that both the hierarchical structure of the goals and the parameter sharing facilitated by relational models significantly improve the learning speed of the agent. Finally, we present the results of deploying our relational hierarchical model in a real-world activity recognition task.

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Présentation de l'éditeur

Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the principles of decision theory to model the general problem of intelligent assistance. We use a combination of hierarchical task models and probabilistic relational languages to specify prior knowledge of the computer assistant. The assistant exploits its prior knowledge to infer the user's goals and takes actions to assist the user. We evaluate the decision theoretic assistance model in three different domains including a real-world domain to demonstrate its generality. We show through experiments that both the hierarchical structure of the goals and the parameter sharing facilitated by relational models significantly improve the learning speed of the agent. Finally, we present the results of deploying our relational hierarchical model in a real-world activity recognition task.

Biographie de l'auteur

Sriraam Natarajan is currently a Post-Doctoral Research Associate at the University of Wisconsin, Madison. He completed his Ph.D at Oregon State University working with Dr.Prasad Tadepalli. His research interests lie in the fields of Statistical Relational learning, Graphical Models, Reinforcement Learning, User Modeling and Planning.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639145801
ISBN 13 
9783639145809
Reliure
Broché
Nombre de pages
176
Coordonnées du fabricant
non disponible
Personne responsable
non disponible